Generate and manipulate faces with StyleGANEX
Analyze if an image contains a deepfake face
Identify and track faces in a live video stream
Next generation image and video face swapper
Mark faces in images and videos to show key landmarks
opp
Replace faces in videos with new ones
Recognize faces and check face liveness
Identify people with and without masks in images
Swap faces in a video using an image
Classify faces as male or female in images
Detect faces in an image from a URL
emotion recognition
StyleGANEX is a state-of-the-art tool designed for generating and manipulating high-quality faces using advanced generative adversarial networks (GANs). It builds upon the foundation of StyleGAN and introduces Cooperative GANs of Contra and StyleSpace for improved results. StyleGANEX is primarily used in face recognition and generation tasks, enabling users to create realistic and diverse facial images.
• Multi-Domain Support: Generate faces across multiple domains, including different ethnicities, ages, and lighting conditions.
• StyleSpace Control: Fine-tune generated faces using a robust style space for precise control over facial features.
• High-Resolution Images: Produce high-quality, realistic images with exceptional detail.
• Interpretable Edits: Make meaningful edits to generated faces using intuitive controls.
• Flexible Customization: Adjust various parameters to tailor outputs to specific needs.
pip install styleganex to install the package.import styleganex in your Python script to access the tool.model = StyleGANEX().model.generate() to create new faces. You can customize outputs by passing specific parameters (e.g., seed, style, etc.).What makes StyleGANEX different from other GANs?
StyleGANEX stands out due to its StyleSpace framework, which allows for precise control over facial features, enabling more interpretable and customizable generation.
Can I use StyleGANEX for non-face generation tasks?
While StyleGANEX is primarily designed for face generation, it can be adapted for other tasks with proper fine-tuning and domain-specific training.
How can I evaluate the quality of generated faces?
Use metrics like FID (Frechet Inception Distance) or IS (Inception Score) to evaluate the quality and diversity of generated faces.